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    "# 对话式RAG\n",
    "##### 本指南假设你熟悉一下概念：\n",
    "- [聊天历史](https://www.langchain.com.cn/docs/concepts/#chat-history)\n",
    "- [聊天模型](https://www.langchain.com.cn/docs/concepts/#chat-models)\n",
    "- [嵌入](https://www.langchain.com.cn/docs/concepts/#embedding-models)\n",
    "- [向量存储](https://www.langchain.com.cn/docs/concepts/#vector-stores)\n",
    "- [检索增强生成RAG](https://www.langchain.com.cn/docs/tutorials/rag/)\n",
    "- [工具](https://www.langchain.com.cn/docs/concepts/#tools)\n",
    "- [代理](https://www.langchain.com.cn/docs/concepts/#agents)\n"
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    "许多问答应用中，我们希望运行用户进行反复对话，这意味着应用需要某种形式的“记忆”来记录过去的问题和答案，并且具备将这些信息融入当前思考的逻辑。\n",
    "在本指南中，我们重点关注**添加用于整合历史消息的逻辑**。\n",
    "\n",
    "我们将介绍两种方法：\n",
    "1. 链接，其中我们始终执行检索步骤\n",
    "2. 代理，其中我们给予大型语言模型自由决定是否以及如何执行检索步骤(或多个步骤)\n",
    "对于外部知识，我们将使用xxxx"
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